OD44B:
Artificial Intelligence Systems for Advancing the Study of Aquatic Ecosystems II Posters

Session ID#: 85898

Session Description:
Scientists studying aquatic ecosystems are increasingly able to collect big data; large and complex datasets necessitating more computing intensive analyses. While the data (e.g., from acoustics or omics) themselves can be quite different, the methods to analyze them are often rather similar. In many cases, artificial intelligence (AI; e.g., machine learning, deep learning) can expedite analyses by limiting the amount of human interaction needed. Furthermore, AI-based analyses are often able to detect patterns that traditional statistics do no€™t pick up on. AI research has begun to surface in all corners of aquatic sciences. Researchers dealing with in situ imagery, and passive and active acoustic data have made particularly rapid progress, but other research areas are also pushing boundaries by applying AI techniques. Examples of such research include ocean -omics research and eDNA, autonomous sampling, fisheries research and management, as well as satellite imagery processing and the automated identification of sea surface features. We invite practitioners from various oceanographic disciplines to submit abstracts highlighting their research on big data and AI at all levels of biological organization (individual, population, ecosystems) and spatio-temporal scales. Given the nascent nature of this field, submissions that focus on methodological innovations are equally welcome to those delving into using AI to address ecological questions.
Co-Sponsor(s):
  • IS - Ocean Observatories, Instrumentation and Sensing Technologies
  • ME - Marine Ecology and Biodiversity
  • PI - Physical-Biological Interactions
Index Terms:

1942 Machine learning [INFORMATICS]
1942 Machine learning [INFORMATICS]
4264 Ocean optics [OCEANOGRAPHY: GENERAL]
4817 Food webs, structure, and dynamics [OCEANOGRAPHY: BIOLOGICAL]
4858 Population dynamics and ecology [OCEANOGRAPHY: BIOLOGICAL]
4894 Instruments, sensors, and techniques [OCEANOGRAPHY: BIOLOGICAL]
Primary Chair:  Moritz S Schmid, Oregon State University, Hatfield Marine Science Center, Newport, OR, United States
Co-chairs:  Eric Coughlin Orenstein, Monterey Bay Aquarium Research Institute, Moss Landing, United States, Christian Briseño-Avena, Oregon State University, Hatfield Marine Science Center, Newport, United States and Emlyn Davies, SINTEF Ocean, Trondheim, Norway
Primary Liaison:  Moritz S Schmid, Oregon State University, Hatfield Marine Science Center, Newport, OR, United States
Moderators:  Christian Briseño-Avena, Oregon State University, Hatfield Marine Science Center, Newport, United States and Emlyn Davies, SINTEF Ocean, Trondheim, Norway
Student Paper Review Liaisons:  Christian Briseño-Avena, Oregon State University, Hatfield Marine Science Center, Newport, United States and Emlyn Davies, SINTEF Ocean, Trondheim, Norway

Abstracts Submitted to this Session:

 
An Open-Source System for Do-It-Yourself AI in the Marine Environment (642487)
Anthony Hoogs1, Matthew David Dawkins2, Benjamin Richards3, George Cutter4, Deborah Hart5, M. Elizabeth Clarke6, William Michaels7, Jon Crall8, Linus Sherrill8, Neil Siekierski9, Matthew Woehlke9 and Kyle Edwards9, (1)Kitware, Clifton Park, United States, (2)Kitware, Saratoga Springs, NY, United States, (3)NOAA, Honolulu, HI, United States, (4)NOAA Southwest Fisheries Science Center, Antarctic Ecosystem Research Division, La Jolla, CA, United States, (5)NOAA Fisheries Woods Hole Laboratory, Woods Hole, United States, (6)NOAA NWFSC, Seattle, WA, United States, (7)NOAA. Fisheries, US DOC, Silver Spring, MD, United States, (8)Kitware, NY, United States, (9)Kitware Inc., Clifton Park, United States
 
Vision-based Real-time Zooplankton Detection and Classification using Fast R-CNN (650858)
Sadaf Ansari1, Aya Saad2, Annette Stahl Prof2 and Madhan Rajachandran1, (1)CSIR-National Institute of Oceanography, Goa, India, (2)Norwegian University of Science and Technology, Engineering Cybernetics, Trondheim, Norway
 
LAPS Plankton Detector: A User-Friendly Computer Vision Tool for Automatic Plankton Identification (656756)
Leandro Ticlia de la Cruz1, Hidekatsu Yamazaki2 and Rubens Mendes Lopes1, (1)University of Sao Paulo, Department of Biological Oceanography, Sao Paulo, Brazil, (2)Tokyo University of Marine Science and Technology, Tokyo, Japan
 
Recent Advances in Visual Sensing and Machine Learning Techniques for in-situ Plankton-taxa Classification (636384)
Aya Saad1, Emlyn Davies2 and Annette Stahl Prof1, (1)Norwegian University of Science and Technology, Engineering Cybernetics, Trondheim, Norway, (2)SINTEF Ocean, Trondheim, Norway
 
A UUV simulator for generating sidescan training data for ANNs (651789)
Hunter C. Brown, L3Harris, Fall River, MA, United States
 
High-temporal resolution in situ imaging and machine learning to observe copepod-parasite interactions (654175)
Eric Coughlin Orenstein1, Christian Briseño-Avena2, Paul Roberts1, Jules S Jaffe3 and Peter J. S. Franks4, (1)Monterey Bay Aquarium Research Institute, Moss Landing, United States, (2)Oregon State University, Hatfield Marine Science Center, Newport, United States, (3)Scripps Institution of Oceanography, La Jolla, CA, United States, (4)Univ California San Diego, La Jolla, United States
 
Prey and predator overlap at the edge of a mesoscale eddy: fine-scale, in-situ distributions to inform our understanding of oceanographic processes (640909)
Moritz S Schmid1, Robert Cowen1, Kelly L Robinson2, Jessica Y Luo3, Christian Briseño-Avena4,5 and Su Sponaugle6, (1)Oregon State University, Hatfield Marine Science Center, Newport, OR, United States, (2)University of Louisiana-Lafayette, Department of Biology, Lafayette, LA, United States, (3)NOAA Geophysical Fluid Dynamics Laboratory, Princeton, United States, (4)Oregon State University, Hatfield Marine Science Center, Newport, United States, (5)University of San Diego, Department of Environmental and Ocean Sciences, San Diego, CA, United States, (6)Oregon State University, Department of Integrative Biology, Corvallis, OR, United States
 
Automated Surveying of Phytoplankton Population Development in a Mesocosm Experiment (657397)
Joe Walker, University of California San Diego, La Jolla, United States; Scripps Institution of Oceanography, La Jolla, United States, Jules S Jaffe, Scripps Institution of Oceanography, La Jolla, CA, United States, Eric Coughlin Orenstein, Monterey Bay Aquarium Research Institute, Moss Landing, United States and Sarah Amiri, University of California Santa Barbara, Santa Barbara, CA, United States
 
Ecosystem Dynamics of the Summer Flounder (636077)
Chryston Best-Otubu, United States
 
Re-training a Joint U-Net-CNN Deep Learning Image Classification Pipeline for the Segmentation of Subsea Macrofauna (656270)
Mitchell Scott1, Bhuvan Malladihalli Shashidhara2 and Aaron Marburg1, (1)Applied Physics Laboratory University of Washington, Seattle, WA, United States, (2)University of Washington, Seattle, WA, United States
 
Investigation of 3D, 4D, and hybrid automated methods to expedite high-precision coral segmentation (657927)
Hugh Runyan1, Vid Petrovic2, Nicole Pedersen1, Clinton Brook Edwards1, Stuart A Sandin1 and Falko Kuester2, (1)Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, United States, (2)California Institute for Telecommunications and Information Technology, UC San Diego, La Jolla, CA, United States
 
Leveraging automated image analysis tools to transform our capacity to assess status and trends on coral reefs. (640957)
Courtney Couch1,2, Ivor Williams3, Oscar Beijbom4, Thomas Oliver2, Bernardo Vargas-Angel5, Brett Schumacher6 and Rusty Eugene Brainard7, (1)Joint Institute for Marine and Atmospheric Research, Honolulu, HI, United States, (2)NOAA Pacific Islands Fisheries Science Center, Ecosystem Sciences Division, Honolulu, HI, United States, (3)NOAA Pacific Islands Fisheries Science Center, Ecosystem Sciences Division, Honolulu, United States, (4)University of California San Diego, La Jolla, CA, United States, (5)National Oceanic and Atmospheric Administration, Ecosystem Sciences Division, Honolulu, HI, United States, (6)NOAA Pacific Islands Regional Office, Sustainable Fisheries Division, Honolulu, United States, (7)NOAA Fisheries, Pacific Islands Fisheries Science Center, Honolulu, HI, United States
 
Probabilistic Habitat Modeling for Benthic Surveys (657553)
Jackson Shields, University of Sydney, Australian Centre for Field Robotics, Sydney, NSW, Australia, Oscar Pizarro, ACFR, University Of Sydney, Australia and Stefan B Williams, The University of Sydney, Australian Centre for Field Robotics, Sydney, NSW, Australia
 
Artificial Intelligence and Computer Vision for Cost-Effective Benthic Habitat Characterization (657342)
Brandon Sackmann1, Eugene Revelas2, Kenia Whitehead1 and Craig Alexander Jones3, (1)Integral Consulting Inc., Olympia, WA, United States, (2)Integral Consulting Inc., United States, (3)Integral Consulting Inc., Santa Cruz, CA, United States